guilherme c. pena marcus v. a. andrade salles v. g ...wrf/p/177-iceis2014-pena-site-gpu-talk.pdf ·...

Guilherme C. Pena Marcus V. A. Andrade

Salles V. G. Magalhães W. Randolph Franklin

Chaulio R. Ferreira

Universidade Federal de Viçosa (UFV) Rensselaer Polytechnic Institute (RPI)

RPI  UFV  

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Introduction

q  Visibility applications play an important role in Geographical Information Systems (GIS).

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Introduction

q  Visibility applications play an important role in Geographical Information Systems (GIS).

q  The focus is to find the points on the terrain that

are visible from a particular point (the observer).

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Introduction q  These applications include telecommunications,

security monitoring, observation paths, etc.

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Introduction q  These applications include telecommunications,

security monitoring, observation paths, etc.

q  For example, an “observer” may be a mobile phone tower

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Introduction q  These applications include telecommunications,

security monitoring, observation paths, etc.

q  For example, an “observer” may be a mobile phone tower or an observation tower.

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Introduction q  An important problem is to site observers in order

to obtain an optimal visual coverage of a terrain.

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Introduction q  An important problem is to site observers in order

to obtain an optimal visual coverage of a terrain.

q  For example, suppose that you want to cover 95% of a terrain.

q  How many and where to site observers to achieve this coverage?

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Introduction q  We will present a parallel method to solve a

variation of the siting observers problem on terrains represented by a digital elevation matrix.

Terrain Digital Elevation Matrix

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Terrain visibility q  An observer is a point (in the space) from which

we wish to see or communicate with other points, called targets.

q  The radius of interest, R, of an observer means the distance that the observer can see.

q  For example, for an observation tower, R is the maximum distance that a person on the tower can see.

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Terrain visibility q  A point is visible by the observer if its distance

from the observer is, at most, R, and if there is no terrain point blocking the line segment connecting the point and the observer.

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Terrain visibility q  A point is visible by the observer if its distance

from the observer is, at most, R, and if there is no terrain point blocking the line segment connecting the point and the observer.

q  For example,

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Terrain visibility q  A point is visible by the observer if its distance

from the observer is, at most, R, and if there is no terrain point blocking the line segment connecting the point and the observer.

q  For example,

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Terrain visibility q  A point is visible by the observer if its distance

from the observer is, at most, R, and if there is no terrain point blocking the line segment connecting the point and the observer.

q  For example,

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Terrain visibility

Terrain visualization Viewshed

q  The viewshed of an observer is the set of terrain points whose corresponding targets are visible from it.

q  The visibility index of an observer is the number of targets that are visible from it.

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Terrain visibility q  The joint viewshed of a set of observers is the union

of the individual viewsheds.

q  The joint visibility index (VIX) of a set of observers is the number of targets that are visible from at least one observer in the set.

Terrain visualization Joint viewshed

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Terrain visibility q  The viewshed and the joint viewshed are (usually)

represented by a square bit matrix of size 2R x 2R.

q  In this matrix, 1 indicates that the corresponding target is visible and 0 is not.

q  Thus, the (joint) visibility index is the number of 1 bits in the matrix.

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Observer siting q  The Multiple Observer Siting Problem: given a

set P of (candidate) observers, select N observers in P such that the joint visibility index of this subset is maximized.

q  Example: selecting 10 observers

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Observer siting q  This problem is NP-Hard. q  It is (generally) solved using a heuristic. q  We propose an efficient local search strategy to

improve the solution obtained by a greedy method.

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Multiple observer siting q  A greedy solution: Site method (Franklin 2002)

•  Given a terrain, let P be a set with the “best” candidate observers;

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Multiple observer siting q  A greedy solution: Site method (Franklin 2002)

•  Given a terrain, let P be a set with the “best” candidate observers;

Those observers with the largest visibility index

Those observers with the highest visibility index

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Multiple observer siting q  A greedy solution: Site method (Franklin 2002)

•  Given a terrain, let P be a set with the “best” candidate observers;

•  Initialize the solution S as empty;

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Multiple observer siting q  A greedy solution: Site method (Franklin 2002)

•  Given a terrain, let P be a set with the “best” candidate observers;

•  Initialize the solution S as empty;

•  Then, iteratively, select the observer (in P) that will most increase the current joint visibility index of S and insert this observer in S;

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Multiple observer siting q  A greedy solution: Site method (Franklin 2002)

•  Given a terrain, let P be a set with the “best” candidate observers;

•  Initialize the solution S as empty;

•  Then, iteratively, select the observer (in P) that will most increase the current joint visibility index of S and insert this observer in S;

•  Repeat the last operation until a termination condition is satisfied.

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Multiple observer siting q  A greedy solution: Site method (Franklin 2002)

•  Given a terrain, let P be a set with the “best” candidate observers;

•  Initialize the solution S as empty;

•  Then, iteratively, select the observer (in P) that will most increase the current joint visibility index of S and insert this observer in S;

•  Repeat the last operation until a termination condition is satisfied.

Typically, until a minimum visual coverage has been achieved or a maximum number of observers

has been selected.

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Multiple observer siting q  The solution obtained by the greedy method is

(mostly) not optimal.

q  We propose a strategy (to try) to increase the terrain coverage preserving the number of observers selected.

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Multiple observer siting q  The solution obtained by the greedy method is

(mostly) not optimal.

q  We propose a strategy (to try) to increase the terrain coverage preserving the number of observers selected.

q  This may reduce the number of observers required

to achieve the desired coverage. q  It may represent an important improvement since an

“observer” can be an expensive facility, for example, a communication tower.

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Our propose q  Extend the greedy method including an

improvement step to try to increase the joint visibility index of each current partial solution.

q  This improvement step checks if the joint

visibility index (of a partial solution) can be increased replacing an observer in the solution with another one did not select yet.

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Our propose q  This checking step performs a local search

whose goal is to select the best neighbor solution.

q  A neighbor solution of a solution S is a solution

S’ where an observer in S is replaced with another observer not in S.

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Our propose – local search q  For example: Suppose P with 5 observers whose

viewsheds are V1, V2, …, V5 and let S={V1, V2, V3} be a partial solution. Thus, the neighbors of S are

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Our propose – local search q  In each iteration of the greedy method, the local

search is repeated until to obtain a solution having no better neighbor (a local optimal).

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Our propose – local search q  In each iteration of the greedy method, the local

search is repeated until to obtain a solution having no better neighbor (a local optimal).

q  This approach is very time consuming.

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GPU  parallel  si,ng  algorithm  

Our propose – local search q  In each iteration of the greedy method, the local

search is repeated until to obtain a solution having no better neighbor (a local optimal).

q  This approach is very time consuming.

q  The greedy method requires a lot of processing time.

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GPU  parallel  si,ng  algorithm  

Our propose – local search q  In each iteration of the greedy method, the local

search is repeated until to obtain a solution having no better neighbor (a local optimal).

q  This approach is very time consuming.

q  The greedy method requires a lot of processing time.

In each iteration, it is necessary to check all candidate observers to select the one that will most increase the joint visibility index

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GPU  parallel  si,ng  algorithm  

Our propose – local search q  In each iteration of the greedy method, the local

search is repeated until to obtain a solution having no better neighbor (a local optimal).

q  This approach is very time consuming. q  The greedy method requires a lot of processing

time. q  The local search is still worse: it has to evaluate

all neighbors of each partial solution.

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GPU  parallel  si,ng  algorithm  

Our propose – local search q  In each iteration of the greedy method, the local

search is repeated until to obtain a solution having no better neighbor (a local optimal).

q  This approach is very time consuming. q  The greedy method requires a lot of processing

time. q  The local search is still worse: it has to evaluate

all neighbors of each partial solution.

Each observer (in the partial solution) is replaced with all the other observers non selected yet.

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Local search: an efficient implementation q  The local search bottleneck is the computation

of the visibility index of all neighbor solutions.

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Local search: an efficient implementation q  The local search bottleneck is the computation

of the visibility index of all neighbor solutions. q  Let P = {p1,…,pn} be the candidate set and

S = {s1,…, sk} be a partial solution.

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Local search: an efficient implementation q  The local search bottleneck is the computation

of the visibility index of all neighbor solutions. q  Let P = {p1,…,pn} be the candidate set and

S = {s1,…, sk} be a partial solution. q  The neighbors of S are

S’ij = S \ {si} U {pj}

for all i=1,..,k and j=1,..,n with i ≠ j and pj ∉ S

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Local search: an efficient implementation q  The visibility indices computation can be

subdivided in two steps:

①  Create an array B of size k and for i=1,…,k, store in B[i] the joint viewshed of S \ {si};

②  Create a matrix V of size k x n and for each i=1,…,k and j=1,…,n, with j ≠ i, store in V[i,j] the visibility index of the joint viewshed obtained overlapping B[i] with the viewshed of the observer pj.

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Local search: an efficient implementation q  A straightforward implementation of step 1 is:

for i ← 1 to k do for m ← 1 to k do if m ≠ i then // overlap B[i] with S[m] B[i] ← B[i] S[m]

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Local search: an efficient implementation q  A straightforward implementation of step 1 is:

for i ← 1 to k do for m ← 1 to k do if m ≠ i then // overlap B[i] with S[m] B[i] ← B[i] S[m]

Overlapping two matrices: the

joint viewshed Bi and the viewshed of the observer pm

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Local search: an efficient implementation q  A straightforward implementation of step 1 is:

for i ← 1 to k do for m ← 1 to k do if m ≠ i then // overlap B[i] with S[m] B[i] ← B[i] S[m]

q  This code performs Θ(k2) overlapping operations; q  We can make much better using dynamic

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Local search: an efficient implementation q  Suppose the partial solution S has 5 observers,

that is, S = {S1,…, S5}. q  Then, the computation of B would require the

overlapping of the following viewsheds:

B[1] = S[2] S[3] S[4] S[5]

B[2] = S[1] S[3] S[4] S[5]

B[3] = S[1] S[2] S[4] S[5]

B[4] = S[1] S[2] S[3] S[5]

B[5] = S[1] S[2] S[3] S[4]

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Local search: an efficient implementation q  Suppose the partial solution S has 5 observers,

that is, S = {S1,…, S5}. q  Then, the computation of B would require the

overlapping of the following viewsheds:

B[1] = S[2] S[3] S[4] S[5]

B[2] = S[1] S[3] S[4] S[5]

B[3] = S[1] S[2] S[4] S[5]

B[4] = S[1] S[2] S[3] S[5]

B[5] = S[1] S[2] S[3] S[4]

The matrix with all B’s can be split in the

following way

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Local search: an efficient implementation q  The computation of the matrix storing all B’s can be

rewritten as following:

B[1]= S[2] S[3] S[4] S[5]

B[2]= S[1] S[3] S[4] S[5]

B[3]= S[1] S[2] S[4] S[5]

B[4]= S[1] S[2] S[3] S[5]

B[5]= S[1] S[2] S[3] S[4]

S[1]

S[1] S[2]

S[1] S[2] S[3]

S[1] S[2] S[3] S[4]

S[2] S[3] S[4] S[5]

S[3] S[4] S[5]

S[4] S[5]

S[5] = +

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Local search: an efficient implementation q  The computation of the matrix storing all B’s can be

rewritten as following:

q  Let L be the left (blue) matrix and R be the right (orange) matrix.

q  These two matrices can be computed separately using an efficient iteration.

B[1]= S[2] S[3] S[4] S[5]

B[2]= S[1] S[3] S[4] S[5]

B[3]= S[1] S[2] S[4] S[5]

B[4]= S[1] S[2] S[3] S[5]

B[5]= S[1] S[2] S[3] S[4]

S[1]

S[1] S[2]

S[1] S[2] S[3]

S[1] S[2] S[3] S[4]

S[2] S[3] S[4] S[5]

S[3] S[4] S[5]

S[4] S[5]

S[5] = +

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Local search: an efficient implementation q  Generalizing, for any i = 2,…k-1,

kiii SSSSB ⊕⊕⊕⊕⊕= +− 111

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Local search: an efficient implementation q  Generalizing, for any i = 2,…k-1, Li Ri

iii RLB ⊕=

kiii SSSSB ⊕⊕⊕⊕⊕= +− 111

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Local search: an efficient implementation q  Generalizing, for any i = 2,…k-1, q  And the values of L and R can be computed by

the following recurrences:

L1 = Φ and Li = Li-1 Si-1 for i=2,…,k Rk = Φ and Ri = Si+1 Ri+1 for i=k-1,…,1

Li Ri

iii RLB ⊕=

kiii SSSSB ⊕⊕⊕⊕⊕= +− 111

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Local search: an efficient implementation q  Thus, the step 1 can be computed performing Θ(k) overlapping operations:

•  k to compute L;

•  k to compute R;

•  k to overlap L and R

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Local search: an efficient implementation q  In step 2, to compute the matrix V:

§  each joint viewshed stored in B is overlapped with the viewshed of each candidate observer did not include in the solution yet;

§  the number of 1 bits in the resulting joint viewshed is counted.

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Matrix V computation q  Supposing the viewsheds are linearized and

stored in a matrix P;

q  Each V[i,j ], for i=1,..,k and j=1,…,n, is the number of 1 bits in the overlapping of B[i ] with P[ j ]

1 2 . . . 4R2

1 0 1 . . . 0

⁞ ⁞ ⁞ ⁞ ⁞

i 0 0 . . . 1

⁞ ⁞ ⁞ ⁞ ⁞

k 1 0 . . . 0

1 2 . . . 4R2

1 1 0 . . . 0

⁞ ⁞ ⁞ ⁞ ⁞

j 1 0 . . . 1

⁞ ⁞ ⁞ ⁞ ⁞

n 0 1 . . . 1

B P

⁞

⁞

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Local search: an efficient implementation q  A straightforward implementation of step 2 is:

for i ← 1 to k do for j ← 1 to n do if j ≠ i then // count the number of 1 bits in B[i ] P[ j ] for w ← 1 to 4R2 do V[i,j ] ← V[i,j ]+(B[i,w] or P[ j,w])

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Matrix V computation q  But, considering the transpose of P

q  The computation of V is very similar to the matrix multiplication (replacing the multiplication operator with a bitwise-or)

1 2 . . . 4R2

1 0 1 . . . 0

⁞ ⁞ ⁞ ⁞ ⁞

i 0 0 . . . 1

⁞ ⁞ ⁞ ⁞ ⁞

k 1 0 . . . 0

1 . . . j . . . n

1 1 . . . 1 . . . 0

2 0 . . . 0 . . . 1

⁞ ⁞ ⁞ ⁞ ⁞ ⁞

2R2 0 . . . 1 . . . 1

B PT

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q  Thus, the code for step 2 is:

for i ← 1 to k do for j ← 1 to n do if j ≠ i then // count the number of 1 bits in B[i ] P[ j ] for w ← 1 to 4R2 do V[i,j] ← V[i,j]+(B[i,w] or PT[w,j])

Local search: an efficient implementation

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Local search: an efficient implementation q  The step 2 can be efficiently computed adapting

a very fast GPU matrix multiplication algorithm. q  We adapted the a lgor i thm deve loped

(implemented) by Nvidia in 2013:

•  the multiplication operation was replaced with bitwise-or operation;

•  as the viewsheds are, usually, very sparse

matrices, we included code to avoid loading and processing matrix blocks where all elements are 0;

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Results q  Our algorithm SiteGSM was compared against

two other versions (implementations): Site+ and SiteGPU.

q  Both are also based on the greedy strategy and use local search, but

•  Site+ uses a sequential (CPU) implementation; •  SiteGPU implements some operations using

GPU but it uses only the GPU global memory and does not include dynamic programming.

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Results q  The tests were executed on a computer with

dual Intel Xeon E5-2687 3.1GHz, 128GiB of memory, GPU NVIDIA Tesla Kepler K20x with 2688 cores running Ubuntu 12.04 LTS.

q  We used terrains with 1201 x 1201 points and

3601 x 3601 points (obtained from NASA STRM)

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Results

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Results q  As an additional test, we compared the

execution time of the local search using a conventional approach against our proposed strategy that includes:

•  dynamic programming

•  “matrix multiplication” using GPU

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Results

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Conclusion q  We presented a very fast implementation of a

method to site observers on terrains. q  This implementation is based on a greedy

strategy combined with a local search where we used dynamic programming and GPU parallel implementation.

q  This local search strategy can be used to

improve other heuristics that solves other optimization problems.

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Future work q  Develop parallel implementation using GPU to:

•  compute the viewshed of each observer; •  replace the greedy strategy.

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DPI-UFV

GPU  parallel  si,ng  algorithm  

Thank you Any questions or suggestions?

Acknowledgements Contacts: marcus@ufv.br salles@ufv.br UFV  

Grant IIS-1117277

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